Api Bridge

Turn any REST API into an MCP server so Claude, Cursor, and other AI assistants can use it directly.

This is a production-quality starter kit that wraps JSONPlaceholder (a free REST API) as 4 MCP tools. The real value is the pattern — swap the API client to point at your own API and you have a working MCP server.

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What This Does

You give Claude (or any MCP-compatible AI assistant) access to a REST API through typed, validated tools:

You: "List the 5 most recent posts by user 3"

Claude calls: api_list_posts(user_id=3, limit=5)
→ Fetches GET /posts?userId=3
→ Returns formatted, paginated results

You: "Create a post about MCP servers"

Claude calls: api_create_post(title="Why MCP Servers Matter", body="...", user_id=1)
→ Sends POST /posts with validated payload
→ Returns the created resource

No prompt engineering needed. The AI assistant discovers the tools, validates inputs via Pydantic schemas, and gets structured responses.

Quick Start

Prerequisites: Python 3.10+, uv (recommended) or pip

# Clone and install
git clone https://github.com/BryceEWatson/mcp-api-bridge.git
cd mcp-api-bridge
uv pip install -e ".[dev]"

# Run the server (stdio transport)
python -m api_bridge_mcp.server

# Or run tests
pytest tests/ -v

Add to Claude Desktop — copy this into your claude_desktop_config.json:

{
  "mcpServers": {
    "api-bridge": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-api-bridge", "api-bridge"]
    }
  }
}

Restart Claude Desktop. The 4 tools appear automatically.

The Tools

Tool	Method	What It Does	Key Patterns
api_list_posts	GET /posts	List posts with filtering + pagination	Query params, in-memory pagination, dual format output
api_get_post	GET /posts/{id}	Fetch a post with optional comments	Resource lookup, related data joining
api_create_post	POST /posts	Create a new post	Write operations, input validation
api_update_post	PATCH /posts/{id}	Update post fields	Partial updates, existence checks

Every tool supports response_format: "markdown" (human-readable) or "json" (machine-readable). All inputs are validated with Pydantic v2 models with field constraints. Every tool has MCP annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) set correctly.

Adapting This For Your API

The whole point of this project is to show a repeatable pattern. Here's how to make it work with any REST API:

Step 1: Replace the API client

Edit src/api_bridge_mcp/api_client.py. Change the base URL and add your auth:

class APIClient:
    def __init__(self, base_url: str = "https://api.your-service.com/v1", timeout: int = 30):
        self.base_url = base_url
        self.timeout = httpx.Timeout(timeout)
        self.headers = {"Authorization": f"Bearer {os.environ['YOUR_API_KEY']}"}

The rest of the client (get/post/put/patch, error handling) works unchanged.

Step 2: Define your Pydantic input models

Replace the post-related models in server.py with your domain:

class SearchOrdersInput(BaseModel):
    model_config = ConfigDict(str_strip_whitespace=True, extra="forbid")

    customer_id: Optional[str] = Field(None, description="Filter by customer")
    status: Optional[str] = Field(None, description="Filter by status: pending, shipped, delivered")
    limit: int = Field(20, ge=1, le=100, description="Results per page")

Step 3: Register your tools

Same @mcp.tool decorator pattern — pass a Pydantic model as the single parameter:

@mcp.tool(
    name="orders_search",
    description="Search orders by customer and status.",
    annotations={"readOnlyHint": True, "idempotentHint": True}
)
async def orders_search(params: SearchOrdersInput) -> str:
    async with APIClient() as client:
        query = {}
        if params.customer_id:
            query["customer_id"] = params.customer_id
        if params.status:
            query["status"] = params.status
        orders = await client.get("/orders", params=query)
        return format_orders(orders[:params.limit])

Step 4: Update the Claude Desktop config

Point the config at your new server. That's it.

Project Structure

mcp-api-bridge/
├── README.md                          ← You are here
├── PLAN.md                            ← Design decisions and research
├── pyproject.toml                     ← PEP 621 packaging
├── claude_desktop_config.json         ← Example Claude Desktop config
├── src/
│   └── api_bridge_mcp/
│       ├── __init__.py
│       ├── api_client.py              ← HTTP client (swap this for your API)
│       └── server.py                  ← MCP tools (4 tools, ~530 lines)
└── tests/
    ├── conftest.py                    ← Shared fixtures and mock data
    ├── test_client.py                 ← API client tests (14 tests)
    └── test_tools.py                  ← Tool tests (32 tests)

The architecture separates the API layer (api_client.py) from the MCP layer (server.py). When adapting for a new API, you primarily modify api_client.py and the Pydantic models — the MCP wiring stays the same.

Design Decisions

These are documented in detail in PLAN.md. The short version:

JSONPlaceholder as the demo API — zero friction (no auth, no signup, no rate limits), full CRUD, and obviously a stand-in so the pattern is the focus, not the domain.

Python + FastMCP — the MCP Python SDK's high-level framework. Handles tool registration, input schema generation, and transport automatically. Fewer lines, fewer bugs.

stdio transport — the right default for local-first MCP servers. Add mcp.run(transport="streamable_http", port=8000) for remote deployment.

Pydantic v2 for validation — every tool input is a typed model with constraints. The AI assistant sees the schema and knows exactly what to send.

Dual response formats — markdown for when a human is reading Claude's output, JSON for when another system is consuming it.

Running Tests

# All tests
pytest tests/ -v

# Just the API client tests
pytest tests/test_client.py -v

# Just the tool tests
pytest tests/test_tools.py -v

The unit and tool tests use pytest-httpx to mock HTTP responses — fast and deterministic. The end-to-end tests hit the live JSONPlaceholder API over the real MCP protocol. 74 tests covering input validation, output formatting, pagination, error handling, and full MCP protocol flows.

Built With

• FastMCP — MCP Python SDK
• httpx — async HTTP client
• Pydantic v2 — input validation
• JSONPlaceholder — demo REST API
• pytest + pytest-httpx — testing

About

Built by Bryce Watson — senior engineer (10+ years, ex-eBay) specializing in AI engineering, MCP servers, and production AI systems. Contributor to Anthropic's Python SDK.

• Upwork: upwork.com/freelancers/brycewatson
• Site: brycewatson.com
• GitHub: github.com/BryceEWatson

Need an MCP server built for your API? Get in touch.